fish.py

import torch
import numpy as np
import torch.optim as optim
import os
import time
import utils.evaluate as evaluate
from utils import fish_tools
from tqdm import tqdm
from loguru import logger
from models import fish
from data.data_loader import sample_dataloader
from utils import AverageMeter
from torch import nn
import random

def train(test_loader, train_loader, database_loader,
        query_loader_zs,database_loader_zs, code_length, args):

    print("fish for zero-shot learning")



    model = fish.fish_ResNet50(code_length, args.num_classes,class_mask_rate= 0.7,device=args.device, pretrained=True)
    model.to(args.device)

    if args.optim == 'SGD':
        optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=0.9)
        # optimizer = optim.SGD(model.parameters(), lr=args.lr, weight_decay=args.wd, momentum=args.momen, nesterov=args.nesterov)
    elif args.optim == 'Adam':
        optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.wd)
    # scheduler = optim.lr_scheduler.MultiStepLR(optimizer, args.lr_step)
    # criterion = A_2_net_Loss(code_length, args.gamma, args.batch_size, args.margin, False)
    criterion = nn.CrossEntropyLoss()
    criterion_hash = nn.MSELoss()

    scheduler = optim.lr_scheduler.MultiStepLR(optimizer, args.lr_step)
    # num_retrieval = len(retrieval_dataloader.dataset)
    # U = torch.zeros(args.num_samples, code_length).to(args.device)
    # B = torch.randn(num_retrieval, code_length).to(args.device)
    # retrieval_targets = retrieval_dataloader.dataset.get_onehot_targets().to(args.device)
    # cnn_losses, hash_losses, quan_losses, reconstruction_losses, decorrelation_losses = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()
    # gan_losses = AverageMeter()
    start = time.time()
    best_mAP = 0
    corresponding_mAP_all = 0
    corresponding_zs_mAP = 0
    corresponding_zs_mAP_all = 0
    train_codes = fish_tools.calc_train_codes(database_loader, code_length, args.num_classes)
    for it in range(args.max_iter):
        iter_start = time.time()

        train_dataloader, sample_index = sample_dataloader(train_loader, args.num_samples, args.batch_size, args.root, args.dataset)

        for epoch in range(args.max_epoch):
            model.train()
            ce_loss = 0.0
            pbar = tqdm(enumerate(train_dataloader),total=len(train_dataloader),ncols= 50)

            for batch, (data, targets, index) in pbar:
                data, targets, index = data.to(args.device), targets.to(args.device), index.to(args.device)
                codes = torch.tensor(train_codes[sample_index[index], :]).float().to(args.device)

                optimizer.zero_grad()
                feature_map, outputs_class, outputs_codes = model(data)
                # ------------------------------------------------------------
                attention = torch.sum(feature_map.detach(), dim=1, keepdim=True)
                attention = nn.functional.interpolate(attention, size=(224, 224), mode='bilinear', align_corners=True)
                masks = []
                for i in range(targets.size()[0]):
                    threshold = random.uniform(0.9, 1.0)
                    mask = (attention[i] < threshold * attention[i].max()).float()
                    masks.append(mask)
                masks = torch.stack(masks)
                hide_imgs = data * masks
                _, outputs_hide, _ = model(hide_imgs)
                # ------------------------------------------------------------
                loss_class = criterion(outputs_class, targets.argmax(dim=1))
                loss_class_hide = criterion(outputs_hide, targets.argmax(dim=1))
                loss_codes = criterion_hash(outputs_codes, codes)
                loss = loss_class + loss_codes + loss_class_hide  # 0.1*
                loss.backward()
                optimizer.step()
                ce_loss += loss.item() * data.size(0)

            epoch_loss = ce_loss / len(train_dataloader.dataset.targets)###wp-----

            logger.info('[epoch:{}/{}][loss:{:.4f}]'.format(epoch+1, args.max_epoch,epoch_loss))
        scheduler.step()
        print(optimizer.param_groups[0]['lr'])
        logger.info('[iter:{}/{}][iter_time:{:.2f}]'.format(it+1, args.max_iter, time.time()-iter_start))

    # Evaluate
        # if (it + 1) % 1 == 0 :
        if (it < 35 and (it + 1) % args.val_freq == 0) or (it >= 35 and (it + 1) % 1 == 0):

            query_code = generate_code(model, test_loader, code_length, args.device)
            query_targets = test_loader.dataset.get_onehot_targets()
            # B = generate_code(model, database_loader, code_length, args.device)
            B = torch.from_numpy(train_codes).float().to(args.device)
            db_label= database_loader.dataset.get_onehot_targets()

            zs_test_binary = generate_code(model, query_loader_zs, code_length, args.device)
            zs_test_label = query_loader_zs.dataset.get_onehot_targets()

            zs_db_binary = generate_code(model, database_loader_zs, code_length, args.device)
            zs_db_label = database_loader_zs.dataset.get_onehot_targets()

            db_all_binary = torch.cat((B, zs_db_binary), 0)
            db_all_label = torch.cat((db_label, zs_db_label), 0)

            mAP = evaluate.mean_average_precision(
                query_code.to(args.device),
                B,
                query_targets[:,:args.num_classes].to(args.device),
                db_label[:,:args.num_classes].to(args.device),
                args.device,
                args.topk,
            )
            mAP_all = evaluate.mean_average_precision(
                query_code.to(args.device),
                db_all_binary.to(args.device),
                query_targets.to(args.device),
                db_all_label.to(args.device),
                args.device,
                args.topk,
            )
            zs_mAP_all = evaluate.mean_average_precision(
                zs_test_binary.to(args.device),
                db_all_binary.to(args.device),
                zs_test_label.to(args.device),
                db_all_label.to(args.device),
                args.device,
                args.topk,
            )
            zs_mAP = evaluate.mean_average_precision(
                zs_test_binary.to(args.device),
                zs_db_binary.to(args.device),
                zs_test_label.to(args.device),
                zs_db_label.to(args.device),
                args.device,
                args.topk,
            )

            if mAP > best_mAP:
                best_mAP = mAP
                corresponding_mAP_all = mAP_all
                corresponding_zs_mAP = zs_mAP
                corresponding_zs_mAP_all = zs_mAP_all

                ret_path = os.path.join('checkpoints', args.info, 'best_mAP',str(code_length))
                if not os.path.exists(ret_path):
                    os.makedirs(ret_path)
                torch.save(query_code.cpu(), os.path.join(ret_path, 'query_code.t'))
                torch.save(B.cpu(), os.path.join(ret_path, 'database_code.t'))
                torch.save(query_targets.cpu(), os.path.join(ret_path, 'query_targets.t'))
                torch.save(db_label.cpu(), os.path.join(ret_path, 'database_targets.t'))

                torch.save(zs_test_binary.cpu(), os.path.join(ret_path, 'zs_test_binary.t'))
                torch.save(zs_db_binary.cpu(), os.path.join(ret_path, 'zs_db_binary.t'))
                torch.save(zs_test_label.cpu(), os.path.join(ret_path, 'zs_test_label.t'))
                torch.save(zs_db_label.cpu(), os.path.join(ret_path, 'zs_db_label.t'))

                torch.save(model.state_dict(), os.path.join(ret_path, 'model.pkl'))
                model = model.to(args.device)



            logger.info('[iter:{}/{}][code_length:{}][mAP:{:.5f}][mAP_all:{:.5f}][best_mAP:{:.5f}]'.format(it+1, args.max_iter, code_length, mAP,mAP_all ,best_mAP))
            logger.info('[iter:{}/{}][code_length:{}][zs_mAP:{:.5f}][zs_mAP_all:{:.5f}]'.format(it+1, args.max_iter, code_length, zs_mAP, zs_mAP_all))

            # logger.info('[iter:{}/{}][code_length:{}][mAP:{:.5f}][best_mAP:{:.5f}]'.format(it+1, args.max_iter, code_length, mAP, best_mAP))
            # logger.info('[iter:{}/{}][code_length:{}][zs_mAP:{:.5f}][zs_best_mAP:{:.5f}]'.format(it+1, args.max_iter, code_length, zs_mAP, zs_best_mAP))

    logger.info('[Training time:{:.2f}]'.format(time.time()-start))

    return best_mAP, corresponding_mAP_all, corresponding_zs_mAP, corresponding_zs_mAP_all


def solve_dcc(B, U, expand_U, S, code_length, gamma):
    """
    Solve DCC problem.
    """
    Q = (code_length * S).t() @ U + gamma * expand_U

    for bit in range(code_length):
        q = Q[:, bit]
        u = U[:, bit]
        B_prime = torch.cat((B[:, :bit], B[:, bit+1:]), dim=1)
        U_prime = torch.cat((U[:, :bit], U[:, bit+1:]), dim=1)

        B[:, bit] = (q.t() - B_prime @ U_prime.t() @ u.t()).sign()

    return B


def calc_loss(U, B, S, code_length, omega, gamma):
    """
    Calculate loss.
    """
    hash_loss = ((code_length * S - U @ B.t()) ** 2).sum()
    quantization_loss = ((U - B[omega, :]) ** 2).sum()
    loss = (hash_loss + gamma * quantization_loss) / (U.shape[0] * B.shape[0])

    return loss.item()


def generate_code(model, dataloader, code_length, device):
    """
    Generate hash code

    Args
        dataloader(torch.utils.data.DataLoader): Data loader.
        code_length(int): Hash code length.
        device(torch.device): Using gpu or cpu.

    Returns
        code(torch.Tensor): Hash code.
    """
    model.eval()
    with torch.no_grad():
        N = len(dataloader.dataset)
        code = torch.zeros([N, code_length]).to(device)
        for batch, (data, targets, index) in enumerate(dataloader):
            data, targets, index = data.to(device), targets.to(device), index.to(device)
            _,_,hash_code = model(data)
            code[index, :] = hash_code.sign()
    return code